Industrial environment multi-sensor dataset for vehicle indoor tracking with wi-fi, inertial and odometry data

Citation: Silva, I.; Pendão, C.; Torres-Sospedra, J.; Moreira, A. Industrial Environment Multi-Sensor Dataset for Vehicle Indoor Tracking with Wi-Fi, Inertial and Odometry Data. Data 2023,8, 157. https://doi.org/10.3390/ data8100157 Academic Editors: Giuseppe Ciaburro and Juanle Wang Received: 10 July 2023 Revised: 12 October 2023 Accepted: 17 October 2023 Published: 23 October 2023 Copyright: © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). data Data Descriptor Industrial Environment Multi-Sensor Dataset for Vehicle Indoor Tracking with Wi-Fi, Inertial and Odometry Data Ivo Silva 1,* , Cristiano Pendão 1,2,* , Joaquín Torres-Sospedra 1and Adriano Moreira 1 1Centro ALGORITMI, Universidade do Minho, Campus de Azurém, 4800-058 Guimarães, Portugal; [email protected] (J.T.-S.); [email protected] (A.M.) 2Department of Engineering, University of Trás-os-Montes e Alto Douro, 5000-801 Vila Real, Portugal *Correspondence: [email protected] (I.S.); [email protected] (C.P.) Abstract: This paper describes a dataset collected in an industrial setting using a mobile unit resembling an industrial vehicle equipped with several sensors. Wi-Fi interfaces collect signals from available Access Points (APs), while motion sensors collect data regarding the mobile unit’s movement (orientation and displacement). The distinctive features of this dataset include synchronous data collection from multiple sensors, such as Wi-Fi data acquired from multiple interfaces (including a radio map), orientation provided by two low-cost Inertial Measurement Unit (IMU) sensors, and displacement (travelled distance) measured by an absolute encoder attached to the mobile unit’s wheel. Accurate ground-truth information was determined using a computer vision approach that recorded timestamps as the mobile unit passed through reference locations. We assessed the quality of the proposed dataset by applying baseline methods for dead reckoning and Wi-Fi fingerprinting. The average positioning error for simple dead reckoning, without using any other absolute positioning technique, is 8.25 m and 11.66 m for IMU1 and IMU2, respectively. The average positioning error for simple Wi-Fi fingerprinting is 2.19 m when combining the RSSI information from five Wi-Fi interfaces. This dataset contributes to the fields of Industry 4.0 and mobile sensing, providing researchers with a resource to develop, test, and evaluate indoor tracking solutions for industrial vehicles. Keywords: Industry 4.0; datasets; fingerprinting; motion sensors; industrial vehicles; indoor tracking; indoor positioning; Wi-Fi; IMU; encoder 1. Introduction Industry 4.0 is a generation of manufacturing development driven by disruptive trends in numerous areas, including the Internet of Things (IoT) [ 1 ], deep learning [ 2 ], and edge computing [ 3 ], among others [ 4 ]. Industrial vehicles have an important role in manufacturing, participating in the transport of raw materials and finished goods as well as moving materials within factories. Factories benefit from tracking vehicles to monitor and control operations, improve logistics and safety, and also to enable inter-connectivity between vehicles, other machines and even human operators. Contrarily to outdoor environments where Global Positioning System (GPS) is used to locate vehicles [ 5 ], in indoor environments, satellite-based positioning systems are not reliable. Alternatively, indoor vehicles can be localised using Wi-Fi-based positioning systems [ 6 ]. Wi-Fi is ubiquitous, being present in most environments, including industrial buildings; hence, its infrastructure can be explored for localisation without additional costs. Wi-Fi fingerprinting [ 7 , 8 ] is one of the most used positioning techniques based on WiFi. It consists of two phases, the calibration (or offline) phase and the online phase. In the calibration phase, a radio map is built by collecting Wi-Fi samples in known locations, which are also known as reference points. In the online phase, an operational sample is compared against the radio map using a (dis)similarity function to find the radio map samples that are more similar to the operational sample. Then, a position estimate is obtained using Data 2023,8, 157. https://doi.org/10.3390/data8100157 https://www.mdpi.com/journal/data Data 2023,8, 157 2 of 20 an algorithm (e.g., the k-Nearest Neighbour (k-NN)), based on the most similar radio map samples. Despite being low cost and simple to implement, Wi-Fi fingerprinting has a few drawbacks: namely, it requires the collection of the radio map, and it is prone to large errors [ 9 ], which occur due to numerous reasons such as changing the indoor layout, adding/removing APs, or propagation effects. Although Wi-Fi may provide an absolute position, additional sensors are necessary to accurately track industrial vehicles. The vehicle’s movement can be tracked using sensors that measure the heading (orientation) and the displacement (travelled distance). IMU sensors allow measuring the absolute heading of the vehicle, detecting which way it is going towards, and wheel encoders may be used as odometers to measure the travelled distance. The vehicle’s trajectory based on motion sensors can be determined with dead reckoning. An initial position must be provided, and then upon receiving new observations, the trajectory is updated based on the previous position. This approach is prone to cumulative errors because the IMU sensor is affected by bias and noise, leading to drift in the heading. Therefore, the position should be corrected after some time to avoid large errors. Combining Wi-Fi with motion sensor data mitigates the drawbacks of both Wi-Fi fingerprinting and dead reckoning [ 10 ]. For instance, Wi-Fi fingerprinting can provide the absolute position necessary for the initial position of dead reckoning and correct the drift in the estimated trajectory. In addition, large positioning errors may be reduced when combining Wi-Fi with motion sensor data. Kalman filters and particle filters are two of the most-known sensor fusion approaches to fuse Wi-Fi with motion sensors [6,11,12]. Testing and evaluation are crucial parts of the development of an Indoor Positioning System (IPS), usually requiring real-world experiments to collect data for validation and testing. When the objective of the IPS is to localise and track industrial vehicles equipped with multiple sensors, this process comprises several stages: 1. Preparing the setup where experiments are conducted: (a) Defining the reference points for the radio map; (b) Mapping the locations of the APs and reference points, measuring their positions with respect to the building’s coordinate reference system. 2. Preparing the hardware and software: (a) Physically connect all sensors to a computer; (b) Develop sensor acquisition software; (c) Test software collecting data from all sensors simultaneously. 3. Perform calibration by collecting the radio map at reference points; 4. Collect the sensor data and ground truth in multiple trajectories. There are several public Wi-Fi datasets for Wi-Fi-based positioning systems, [ 13 – 19 ], which are collected in a variety of scenarios, including universities, office buildings, shopping malls, and industrial factory-like space. There are also hybrid datasets, namely, with Wi-Fi and Bluetooth Low Energy (BLE) data [ 20 ], with Wi-Fi, BLE, and Zigbee [ 21 ], with Wi-Fi, BLE, and magnetometer data [ 22 ], with BLE and IMU data [ 23 ], or even with Wi-Fi, BLE, cellular signal and multi-sensor data (magnetometer, accelerometer, gyroscope, barometer, and ambient light sensor) [ 24 ]. The International Conference on Indoor Positioning and Indoor Navigation (IPIN) provides their competitions’ datasets, containing multi-sensor data along with ground truth [ 25 – 34 ], which are available at https://ipin-conference.org/resources.html (accessed on 7 July 2023). In 2021, Microsoft co-organised the Indoor Location Competition 2.0 . The competition introduced a firstof-its-kind large-scale indoor location benchmark dataset [35] , containing dense indoor signatures of Wi-Fi, an geomagnetic field, BLE iBeacons, and ground truth locations collected by smartphones from numerous buildings in Chinese cities. Despite the existence of these public datasets, none of them incorporate data gathered specifically in an industrial setting from multiple sensor types, including Wi-Fi. Public access datasets are usually collected at office buildings, laboratories or controlled scenarios. Therefore, they are not considering some relevant features present in industrial layouts, such as wide-open areas Data 2023,8, 157 3 of 20 with metallic machinery, high ceilings and constraints on where to place the Wi-Fi APs. Existing datasets on office buildings are useful for the research community but often include narrow large corridors, small offices and locations of APs that do not mimic a real industrial setup. Additionally, while datasets for mobile robots in indoor environments exist, encompassing data from laser scanners, cameras, and odometry [ 36 , 37 ], none of these datasets are tailored for indoor vehicles equipped with Wi-Fi interfaces and motion sensors to accurately track their movement. In industrial settings, it is difficult to conduct real-world experiments in the production area where operators, robots and vehicles are moving in space; therefore, datasets are essential for the development, prototyping and validation of novel IPSs. This allows researchers to focus mostly on the development of the IPSs instead of needing to perform the above-mentioned stages to prepare the experiment’s setup and collect sensor data. In this paper, we present a dataset for vehicle tracking in indoor environments, which includes Wi-Fi and motion sensor data. We fully describe the dataset, how it was collected, and show examples of data usage. To the best of our knowledge, there is no other open-source dataset with these characteristics: (1) data collection at an industrial building with large open spaces and heavy machinery; (2) Wi-Fi data from multiple synchronous Wi-Fi interfaces, which has been shown to improve positioning performance [ 38 ]; (3) orientation data from two low-cost IMUs, providing raw accelerometer, gyroscope, and magnetometer data as well as absolute orientation provided by integrated sensor fusion algorithms; (4) displacement data provided by an absolute encoder attached to the wheel; and (5) ground-truth data determined by a computer vision approach which automates the annotation process and improves accuracy by providing not only the position but also the orientation of the mobile unit. The objective of this paper is to share this indoor tracking dataset with the community, providing an additional resource for the development, evaluation and benchmarking of IPSs. This contribution is especially valuable for the research community focused on positioning solutions for Industry 4.0 and IoT. A potential use of this dataset is for the development of solutions for vehicle automation and mobile robots, as it contains data from sensors commonly found in these solutions. This dataset will also enable the research community to test their Machine/Deep Learning proposals for PDR [ 39 , 40 ], Wi-Fi fingerprinting [ 41 , 42 ] or a combination of both [43,44] with external datasets. The remainder of this paper is organised as follows. A detailed description of the dataset is made in Section 2. The data collection approach is presented in Section 3, describing the building where data were collected and the software and hardware necessary to collect and store the data. An analysis of the data is made in Section 4, providing several details and statistics about the dataset. Examples of two possible data uses are provided in Section 5, using two baseline methods, namely, dead reckoning and Wi-Fi fingerprinting. Finally, the conclusions are presented in Section 6. 2. Data Description The dataset is structured in several folders inside the data folder, whose contents are as follows: •T1 – T6 trajectory sub-folders—each sub-folder contains the data relative to a trajectory; •RadioMap sub-folder—contains a set of Wi-Fi samples collected at known locations, also known as radio map, which can be used for Wi-Fi fingerprinting-based positioning systems; •aps.csv —contains the information regarding the Wi-Fi APs detected in the building. APs whose IDs start with 2, e.g., 2XXX , are the ones whose position is not known. The positions of the other nine APs (IDs 0XXX and 1XXX) are included in this file; •tags.csv—contains the information regarding the radio map reference points; •floor_plan.jpg —the image of the building’s floor plan, which can be used for visualisation purposes or sensor fusion approaches that explore the floor plan information (e.g., particle filters); Data 2023,8, 157 4 of 20 •floor_plan_cal.jpg —the image of the building’s floor plan with four calibration points marked, which can be helpful for plotting and using the floor plan information; •floor_plan_cal.csv —contains coordinates of the calibration points, which are marked in the floor_plan_cal.jpg image. 2.1. Trajectory Files Each trajectory sub-folder contains nine sub-sets of data (sub-files), as follows: Dj={E,I1,I2,W1,W2,W3,W4,Wm,GT}(1) where j defines the number of the trajectory with j∈{1,...,6} . E represents the set of samples from the encoder sensor in the enc.csv file; I1 and I2 represent the set of samples from both IMU sensors in imu_1.csv and imu_2.csv , respectively; W1 , W2 , W3 , and W4 represent the set of samples from Wi-Fi interfaces in rss_1.csv , rss_2.csv , rss_3.csv , and rss_4.csv , respectively; Wm represents the set of averaged Received Signal Strength (RSS) values from all Wi-Fi interfaces in rss_m.csv ; and GT represents the set of Ground Truth (GT) records in ground_truth.csv. 2.1.1. Encoder Data Encoder samples in the enc.csv file are defined as follows: e={t,d,θ}(2) where t represents the time in milliseconds, d represents the displacement since the previous sample (in meters), and θ defines the absolute angle at which the wheel is positioned at (value between 0 and 2 π , in radians). An example of the encoder data from a CSV file is provided in Figure 1. Data 2023,1, 0 4 of 21 •floor_plan_cal.jpg – the image of the building’s floor plan, with four calibration points marked, which can be helpful for plotting and using the floor plan information; •floor_plan_cal.csv – contains coordinates of the calibration points, marked in the floor_plan_cal.jpg image. 2.1. Trajectory Files Each trajectory sub-folder contains nine sub-sets of data (sub-files), as follows: Dj={E,I1,I2,W1,W2,W3,W4,Wm,GT}(1) where j defines the number of the trajectory with j∈{1,...,6} . E represents the set of samples from the encoder sensor in the enc.csv file; I1 and I2 represent the set of samples from both IMU sensors in imu_1.csv and imu_2.csv , respectively; W1 , W2 , W3 , and W4 represent the set of samples from Wi-Fi interfaces in rss_1.csv , rss_2.csv , rss_3.csv , and rss_4.csv , respectively; Wm represents the set of averaged Received Signal Strength (RSS) values from all Wi-Fi interfaces in rss_m.csv ; and GT represents the set of Ground Truth (GT) records in ground_truth.csv. 2.1.1. Encoder Data Encoder samples in the enc.csv file are defined as: e={t,d,θ}(2) where t represents the time in milliseconds, d represents the displacement since the previous sample (in meters), and θ defines the absolute angle at which the wheel is positioned at (value between 0 and 2π, in radians). time,displacement,angle 0.000,0.001199913860746,2.672099084803318 0.021,0.001090830782496,2.689552377323262 0.041,0.001418080017245,2.712241657599188 0.061,0.001418080017245,2.734930937875114 0.081, ... Figure 1. Example of Encoder data file from trajectory T1. 2.1.2. IMU Data Inertial Measurement Unit (IMU) samples in the imu_i.csv files are defined as: imu ={t,qw,qx,qy,qz,roll,pitch,yaw,gyrx,gyry,gyrz,accx,accy,accz,magx,magy,magz} (3) where t represents the time in milliseconds, (qw , qx , qy , qz) represent the orientation in quaternion format, (roll , pitch , yaw) represent the orientation in Euler angles (degrees), and gyr , acc , and mag tri-axis values represent the raw data from the gyroscope, accelerometer, and magnetometer sensors, that measure the angular rate (rad/s), acceleration (m/s 2 ) and magnetic field (µT), respectively. time,w,x,y,z,roll,pitch,yaw,gyro_x,gyro_y,gyro_z,acce_x,acce_y,acce_z,magn_x,magn_y,magn_z 0.038,0.999755859375,-0.0228271484375,0.003173828125,-0.0001220703125,-0.3125,2.625,90.0,0.0,0.0,0.0087266462599716,0.43,-0.5700000000000001,9.44,-84.6875,-2.5625,-18.5625 0.089,0.999755859375,-0.02313232421875,0.00152587890625,-0.0001220703125,-0.125,2.625,90.0,0.0054541539124822,0.0010908307824964,0.0098174770424681,0.43,-0.39,9.45,-83.5625,-3.25,-18.875 0.137,0.999755859375,-0.02313232421875,0.00128173828125,0.000244140625,-0.0625,2.625,90.0625,-0.0010908307824964,0.0032724923474893,0.0229074464324255,0.28,-0.4,9.62,-84.0,-2.875,-18.5625 0.192,0.999755859375,-0.02301025390625,0.0008544921875,0.0006103515625,-0.0625,2.625,90.125,0.0021816615649929,0.0021816615649929,0.0087266462599716,0.25,-0.36,9.35,-51.5625,-1.75,-10.5 0.241, ... Figure 2. Example of IMU data file from T1. 2.1.3. Wi-Fi Data Wi-Fi samples in the rss_i.csv files are defined as: w={t,rss1, . . . , rssN}(4) where t represents the time in milliseconds, and rssi represents the Received Signal Strength ( RSS ) value of the i -th Access Point ( AP ) (in dBm), with a total of 27 AP s. A default RSS of −120 dBm is assigned to the APs that were not detected in the Wi-Fi sample. Figure 1. Example of encoder data file from trajectory T1. 2.1.2. IMU Data IMU samples in the imu_i.csv files (Figure 2) are defined as follows: imu ={t,qw,qx,qy,qz,roll,pitch,yaw,gyrx,gyry,gyrz,accx,accy,accz,magx,magy,magz}(3) where t represents the time in milliseconds, (qw , qx , qy , qz) represent the orientation in quaternion format, (roll , pitch , yaw) represent the orientation in Euler angles (degrees), and gyr , acc , and mag tri-axis values represent the raw data from the gyroscope, accelerometer, and magnetometer sensors, which measure the angular rate (rad/s), acceleration (m/s 2 ) and magnetic field ( µ T), respectively. An example of the IMU data from a CSV file is provided in Figure 2. Data 2023,1, 0 4 of 21 •floor_plan_cal.jpg – the image of the building’s floor plan, with four calibration points marked, which can be helpful for plotting and using the floor plan information; •floor_plan_cal.csv – contains coordinates of the calibration points, marked in the floor_plan_cal.jpg image. 2.1. Trajectory Files Each trajectory sub-folder contains nine sub-sets of data (sub-files), as follows: Dj={E,I1,I2,W1,W2,W3,W4,Wm,GT}(1) where j defines the number of the trajectory with j∈{1,...,6} . E represents the set of samples from the encoder sensor in the enc.csv file; I1 and I2 represent the set of samples from both IMU sensors in imu_1.csv and imu_2.csv , respectively; W1 , W2 , W3 , and W4 represent the set of samples from Wi-Fi interfaces in rss_1.csv , rss_2.csv , rss_3.csv , and rss_4.csv , respectively; Wm represents the set of averaged Received Signal Strength (RSS) values from all Wi-Fi interfaces in rss_m.csv ; and GT represents the set of Ground Truth (GT) records in ground_truth.csv. 2.1.1. Encoder Data Encoder samples in the enc.csv file are defined as: e={t,d,θ}(2) where t represents the time in milliseconds, d represents the displacement since the previous sample (in meters), and θ defines the absolute angle at which the wheel is positioned at (value between 0 and 2π, in radians). time,displacement,angle 0.000,0.001199913860746,2.672099084803318 0.021,0.001090830782496,2.689552377323262 0.041,0.001418080017245,2.712241657599188 0.061,0.001418080017245,2.734930937875114 0.081, ... Figure 1. Example of Encoder data file from trajectory T1. 2.1.2. IMU Data Inertial Measurement Unit (IMU) samples in the imu_i.csv files are defined as: imu ={t,qw,qx,qy,qz,roll,pitch,yaw,gyrx,gyry,gyrz,accx,accy,accz,magx,magy,magz} (3) where t represents the time in milliseconds, (qw , qx , qy , qz) represent the orientation in quaternion format, (roll , pitch , yaw) represent the orientation in Euler angles (degrees), and gyr , acc , and mag tri-axis values represent the raw data from the gyroscope, accelerometer, and magnetometer sensors, that measure the angular rate (rad/s), acceleration (m/s 2 ) and magnetic field (µT), respectively. time,w,x,y,z,roll,pitch,yaw,gyro_x,gyro_y,gyro_z,acce_x,acce_y,acce_z,magn_x,magn_y,magn_z 0.038,0.999755859375,-0.0228271484375,0.003173828125,-0.0001220703125,-0.3125,2.625,90.0,0.0,0.0,0.0087266462599716,0.43,-0.5700000000000001,9.44,-84.6875,-2.5625,-18.5625 0.089,0.999755859375,-0.02313232421875,0.00152587890625,-0.0001220703125,-0.125,2.625,90.0,0.0054541539124822,0.0010908307824964,0.0098174770424681,0.43,-0.39,9.45,-83.5625,-3.25,-18.875 0.137,0.999755859375,-0.02313232421875,0.00128173828125,0.000244140625,-0.0625,2.625,90.0625,-0.0010908307824964,0.0032724923474893,0.0229074464324255,0.28,-0.4,9.62,-84.0,-2.875,-18.5625 0.192,0.999755859375,-0.02301025390625,0.0008544921875,0.0006103515625,-0.0625,2.625,90.125,0.0021816615649929,0.0021816615649929,0.0087266462599716,0.25,-0.36,9.35,-51.5625,-1.75,-10.5 0.241, ... Figure 2. Example of IMU data file from T1. 2.1.3. Wi-Fi Data Wi-Fi samples in the rss_i.csv files are defined as: w={t,rss1, . . . ,rssN}(4) where t represents the time in milliseconds, and rssi represents the Received Signal Strength ( RSS ) value of the i -th Access Point ( AP ) (in dBm), with a total of 27 AP s. A default RSS of −120 dBm is assigned to the APs that were not detected in the Wi-Fi sample. Figure 2. Example of IMU data file from T1. 2.1.3. Wi-Fi Data Wi-Fi samples in the rss_i.csv files are defined as follows: w={t,rss1, . . . ,rssN}(4) Data 2023,8, 157 5 of 20 where t represents the time in milliseconds, and rssi represents the RSS value of the i -th AP (in dBm), with a total of 27 APs. A default RSS of − 120 dBm is assigned to the APs that were not detected in the Wi-Fi sample. An example of the Wi-Fi data from a CSV file is provided in Figure 3. Data 2023,1, 0 5 of 21 time,0001,0002,0003,1001,1002,1003,1004,1005,1006,2000,2001,2002,2003,2004,2005,2006,2007,2008,2009,2010,2011,2012,2013,2014,2015,2016,2017 0.701,-50,-52,-28,-32,-41,-44,-37,-28,-32,-33,-33,-55,-52,-77,-120,-120,-120,-120,-120,-120,-24,-120,-120,-120,-120,-120,-120 2.319,-49,-56,-34,-27,-41,-41,-32,-25,-27,-32,-32,-53,-52,-80,-120,-120,-120,-120,-120,-120,-29,-120,-120,-120,-120,-120,-120 4.005,-49,-56,-39,-24,-40,-42,-35,-25,-25,-29,-30,-52,-52,-82,-120,-120,-120,-120,-120,-120,-27,-120,-120,-120,-120,-120,-120 5.614,-50,-50,-39,-27,-38,-42,-35,-22,-32,-32,-33,-52,-50,-82,-120,-120,-120,-120,-120,-120,-29,-120,-120,-120,-120,-120,-120 7.234,... Figure 3. Example of Wi-Fi data file from T1. 2.1.4. Ground Truth Data The location of the trajectory samples, also known as ground truth positions ( GT ) in the ground_truth.csv file, is defined as: gt =t,tag,px,py,pz,φ(5) where t represents the time in milliseconds, tag is the identifier of the point, (px , py , pz) are the Cartesian coordinates of the Ground Truth ( GT ) position, and φ is the GT orientation (in degrees). time,tag,x,y,z,orientation 0.033,42,-5.1746,5.1034,0.975,90.2836 0.066,42,-5.1746,5.1037,0.975,90.2836 0.100,42,-5.1741,5.1046,0.975,90.0000 0.133,42,-5.1736,5.1058,0.975,89.7149 0.166, ... Figure 4. Example of ground truth data file from T1. 2.2. Radio Map The radio map sub-folder includes the six sub-sets of data, as follows: RM ={W1,W2,W3,W4,Wm,RP}(6) where Wi represents the set of samples from each Wi-Fi interface, Wm represents the set of averaged RSS values from all Wi-Fi interfaces, and RP represents the set of reference points where Wi-Fi samples were collected. Wi-Fi samples included in rss_i.csv files are defined by Eq. 4, and the reference points included in ref_pos.csv file have the same format as GT points (Eq. 5). The radio map is made of 40 Wi-Fi samples per reference point, with 10 Wi-Fi samples collected at each of four directions turning clockwise, as shown in Figure 5. The mobile unit was placed on the reference point position (RP), and then it was turned to face each one of the four directions while collecting Wi-Fi samples. Figure 5. Mobile unit orientations while collecting Wi-Fi samples: 10 Wi-Fi samples were collected at each orientation, for a total of 40 Wi-Fi samples at each reference point. 2.3. Supporting Software In addition to the dataset, several Python scripts are provided in the code folder: •data_analysis.py – main file that parses the dataset, performs a statistical analysis, and executes Wi-Fi fingerprinting and dead reckoning algorithms as examples of data usage; •plots.py – contains code to generate all plots presented in this paper; Figure 3. Example of Wi-Fi data file from T1. 2.1.4. Ground Truth Data The location of the trajectory samples, also known as ground truth positions ( GT ) in the ground_truth.csv file, is defined as: gt =t,tag,px,py,pz,φ(5) where t represents the time in milliseconds, tag is the identifier of the point, (px , py , pz) are the Cartesian coordinates of the GT position, and φ is the GT orientation (in degrees). An example of the GT data from a CSV file is provided in Figure 4. Data 2023,1, 0 5 of 21 time,0001,0002,0003,1001,1002,1003,1004,1005,1006,2000,2001,2002,2003,2004,2005,2006,2007,2008,2009,2010,2011,2012,2013,2014,2015,2016,2017 0.701,-50,-52,-28,-32,-41,-44,-37,-28,-32,-33,-33,-55,-52,-77,-120,-120,-120,-120,-120,-120,-24,-120,-120,-120,-120,-120,-120 2.319,-49,-56,-34,-27,-41,-41,-32,-25,-27,-32,-32,-53,-52,-80,-120,-120,-120,-120,-120,-120,-29,-120,-120,-120,-120,-120,-120 4.005,-49,-56,-39,-24,-40,-42,-35,-25,-25,-29,-30,-52,-52,-82,-120,-120,-120,-120,-120,-120,-27,-120,-120,-120,-120,-120,-120 5.614,-50,-50,-39,-27,-38,-42,-35,-22,-32,-32,-33,-52,-50,-82,-120,-120,-120,-120,-120,-120,-29,-120,-120,-120,-120,-120,-120 7.234,... Figure 3. Example of Wi-Fi data file from T1. 2.1.4. Ground Truth Data The location of the trajectory samples, also known as ground truth positions ( GT ) in the ground_truth.csv file, is defined as: gt =t,tag,px,py,pz,φ(5) where t represents the time in milliseconds, tag is the identifier of the point, (px , py , pz) are the Cartesian coordinates of the Ground Truth ( GT ) position, and φ is the GT orientation (in degrees). time,tag,x,y,z,orientation 0.033,42,-5.1746,5.1034,0.975,90.2836 0.066,42,-5.1746,5.1037,0.975,90.2836 0.100,42,-5.1741,5.1046,0.975,90.0000 0.133,42,-5.1736,5.1058,0.975,89.7149 0.166, ... Figure 4. Example of ground truth data file from T1. 2.2. Radio Map The radio map sub-folder includes the six sub-sets of data, as follows: RM ={W1,W2,W3,W4,Wm,RP}(6) where Wi represents the set of samples from each Wi-Fi interface, Wm represents the set of averaged RSS values from all Wi-Fi interfaces, and RP represents the set of reference points where Wi-Fi samples were collected. Wi-Fi samples included in rss_i.csv files are defined by Eq. 4, and the reference points included in ref_pos.csv file have the same format as GT points (Eq. 5). The radio map is made of 40 Wi-Fi samples per reference point, with 10 Wi-Fi samples collected at each of four directions turning clockwise, as shown in Figure 5. The mobile unit was placed on the reference point position (RP), and then it was turned to face each one of the four directions while collecting Wi-Fi samples. Figure 5. Mobile unit orientations while collecting Wi-Fi samples: 10 Wi-Fi samples were collected at each orientation, for a total of 40 Wi-Fi samples at each reference point. 2.3. Supporting Software In addition to the dataset, several Python scripts are provided in the code folder: •data_analysis.py – main file that parses the dataset, performs a statistical analysis, and executes Wi-Fi fingerprinting and dead reckoning algorithms as examples of data usage; •plots.py – contains code to generate all plots presented in this paper; Figure 4. Example of ground truth data file from T1. 2.2. Radio Map The radio map sub-folder includes the six sub-sets of data, as follows: RM ={W1,W2,W3,W4,Wm,RP}(6) where Wi represents the set of samples from each Wi-Fi interface, Wm represents the set of averaged RSS values from all Wi-Fi interfaces, and RP represents the set of reference points where Wi-Fi samples were collected. Wi-Fi samples included in rss_i.csv files are defined by Equation (4), and the reference points included in ref_pos.csv file have the same format as GT points (Equation (5)). The radio map is made of 40 Wi-Fi samples per reference point, with 10 Wi-Fi samples collected at each of four directions turning clockwise, as shown in Figure 5. The mobile unit was placed on the reference point position (RP), and then it was turned to face each one of the four directions while collecting Wi-Fi samples. Figure 5. Mobile unit orientations while collecting Wi-Fi samples: 10 Wi-Fi samples were collected at each orientation for a total of 40 Wi-Fi samples at each reference point. Data 2023,8, 157 6 of 20 2.3. Supporting Software In addition to the dataset, several Python scripts are provided in the code folder: •data_analysis.py —main file that parses the dataset, performs a statistical analysis, and executes Wi-Fi fingerprinting and dead reckoning algorithms as examples of data usage; •plots.py—contains code to generate all plots presented in this paper; •indoor_positioning.py —includes Wi-Fi fingerprinting and dead reckoning algorithms as well as the methods to compute the positioning error; •config.py —used as a configuration file that holds several values to configure, including the path to the dataset folder, the folder where results and plots are saved into and also Wi-Fi fingerprinting parameters, the k values and distance functions, that can be Manhattan (city-block) or Euclidean; •requirements.txt—lists the required Python packages to run the code. 3. Data Collection Approach The main architecture of the data collection system, depicted in Figure 6, represents each module and the software technology used to collect data from each sensor. Data from the Wi-Fi interfaces are obtained from a multi-threaded Java application that saves data from each interface into a separate Comma-Separated Values (CSV) file. Similarly, data from the IMUs are obtained from a Python application, running separately for each IMU sensor. This application is also multi-threaded to avoid blocking when it is writing to the files. Data from IMUs are exported into separate CSV files. Data from the encoder sensor are obtained from a Python application and exported into a CSV file. Ground truth data are obtained from a video that records the gt! tags, placed on the floor, when the mobile unit moves. A Python application processes the video to obtain the true position and heading of the mobile using, which are exported into a CSV file. Java Python 2x IMU4x Wi-Fi GT tags Python Encoder Past Future Ground Truth Python Figure 6. Data collection process: Wi-Fi data are collected from a Java application; data from the two IMUs were collected by a Python application; data from the encoder sensor were obtained from another Python application; ground truth data were collected by a video camera and processed by a Python application. Output data from all applications are exported to CSV files. The sensor samples are synchronised in time because the data collection programs for each sensor are executed on the same computer. Consequently, when retrieving a new sensor record, these programs acquire the timestamp from the operating system, enabling them to share a common time reference. The data collection took place at the PIEP building, which is part of the University of Minho’s Azurém Campus. PIEP (Figure 7) is a Centre for Innovation in Polymer Engineering; hence, it has an open-space area quite similar to a factory plant with plastic extrusion machines. The building measures 20m by 50m and has a Wi-Fi infrastructure with several APs installed inside. All APs are emitting in the 2.4 GHz frequency band. Figure 8shows the floor plan, the positions of APs and their IDs as well as the reference Data 2023,8, 157 7 of 20 points where Wi-Fi samples were collected to build the radio map. These reference points also mark the locations where GT data were collected. Grey areas in the floor plan represent obstacles or non-navigable areas. Figure 7. PIEP building (industrial space) at the University of Minho. 30 25 20 15 10 5 0 5 10 15 20 0 5 10 15 0001 0002 00031001 1002 1003 100410051006 AP Ref. Point Figure 8. Floor plan of the PIEP building, including the deployed APs and radio map reference points. Table 1includes details about the deployed APs, namely, their ID, brand and model, and the transmission channel of each AP in the 2.4 GHz frequency band. These APs are the ones whose positions are known. Despite that, there are also other APs available in the building whose positions are not known. Table 1. Configuration of deployed APs. AP ID Brand/Model Channel Frequency 0001 ORiNOCO AP200 1 2412 MHz 0002 ORiNOCO AP200 6 2432 MHz 0003 ORiNOCO AP200 9 2452 MHz 1001 Cisco Aironet 1100 series 1 2412 MHz 1002 Cisco Aironet 1100 series 6 2432 MHz 1003 Cisco Aironet 1100 series 11 2462 MHz 1004 Cisco Aironet 1100 series 9 2452 MHz 1005 Cisco Aironet 1100 series 3 2422 MHz 1006 Cisco Aironet 1100 series 7 2442 MHz Data 2023,8, 157 8 of 20 3.1. Mobile Unit A manually pushed trolley is used to emulate an industrial vehicle equipped with several sensors. The trolley measures 40 ×60 ×97.5 cm (W × D × H). Figure 9represents the relative positions of the Raspberry Pi (RPi) and sensors with respect to the mobile unit. These devices/sensors are listed below: • 1×Raspberry Pi 3B+: computer that runs programs to collect data from sensors; • 1×Absolute Encoder (US Digital A2): Wheel-attached sensor at 62.5 mm height that measures the displacement; • 2× IMU sensors (Adafruit BNO055): Low-cost IMU sensors providing absolute orientation and/or raw magnetometer, gyroscope, and accelerometer measurements; • 4× Wi-Fi interfaces (Edimax EW7811-Un): external Wi-Fi interfaces compliant with IEEE 802.11 b/g/n (2.4 GHz frequency band). The laptop is used to remotely control the data collection process by connecting to the RPi using the Secure Shell (SSH) protocol. Figure 9. Sensors placement (position) on the mobile unit (depth is minimised to reduce image space). 3.2. Encoder In this work, we opted to use a rotary encoder attached to one of the vehicle’s wheels as an odometer to measure the displacement. Rotary encoders can be absolute or incremental. Absolute encoders allow reading the absolute angle at which the wheel is positioned whenever a new reading is obtained. Incremental encoders generate a series of pulses during movement, which can be converted into a velocity from the previous sample. Another difference between absolute and incremental encoders is that when it is first powered, the absolute encoder always reports a known position (wheel position), while the incremental encoder always starts from zero and acts as a counter for each pulse generated. We selected the US Digital A2 absolute encoder to measure the displacement as it is accurate, reliable, and it is simple to read and convert the measured angles into a distance value. The sample rate was configured to 50Hz , i.e., an interval of 20ms between consecutive readings. Converting Absolute Encoder Angle into Displacement Given that the absolute encoder reports the angle at which the wheel is positioned, at least two angle readings are necessary to determine the angle difference. Assuming that the wheel does less than one complete turn between two consecutive samples, the angle difference can be converted into a distance value as follows: Data 2023,8, 157 9 of 20 d=∠AB 360 ×2πr(7) where d represents the measured displacement in the time interval between t0 and t1 , ∠AB represents the angle (in degrees) measured in the same time interval, and r represents the wheel radius. The mobile unit’s wheels have a radius of r=62.5 mm. 3.3. IMU We opted for low-cost sensors to evaluate their feasibility in real-world applications; therefore, we decided to include two IMU sensors in the mobile unit. We selected the Adafruit BNO055 IMU; since it has several fusion modes and provides the absolute position, it can be easily connected to the RPi, it is low-cost, and it is simple to work with. The IMU’s RPi connection setup is shown in Figure 10. In order to connect two BNO055 sensors to the same RPi device, it is necessary to change the default I2C address in one of the IMUs. One device has the default I2C address, which is 0x28. And the device where the ADR pin is connected to 3.3 V has a default I2C address of 0x29. TheBNO055defaultI2C addressis0x28.Thedevice withtheADRpinisconnected to3.3VhasadefaultI2C addressof0x29. Figure 10. RPi GPIO connection setup to connect the IMUs enabling simultaneous data collection from both sensors. Data collection was performed with a Python program composed of two threads with different purposes. One is responsible for obtaining the data from the sensor, and the other is responsible for writing the data into a CSV file. They implement a producer– consumer approach, where the thread that reads data from the sensor is the producer, and the thread that writes the data to the CSV file is the consumer. This approach reduces possible delays in the data collection process, which would be caused in case the process was synchronous with one thread, because writing to the file could block and cause delays in the data collection. Configuration and reading data from the sensor were permitted by the custom CircuitPython driver for the BNO055 sensor (https://github.com/adafruit/ Adafruit_CircuitPython_BNO055 (accessed on 7 July 2023)). The sensor was configured with the NDOF operation mode (https://www.boschsensortec.com/products/smart-sensors/bno055/ (accessed on 7 July 2023)) that includes a sensor fusion algorithm where the fused absolute orientation data are calculated from the accelerometer, gyroscope and magnetometer. The absolute orientation is provided in quaternion and Euler angles formats. In addition to the absolute orientation, this operation mode also outputs raw data from the accelerometer, magnetometer (compass), and gyroscope. The angle range for the rotation angles for roll, pitch, and yaw varies as follows: • Pitch: −180◦to 180◦; • Roll: −90◦to 90◦(increasing with increasing inclination); • Yaw: −180◦to 180◦(turning clockwise decreases value). Although the sensor outputs the yaw ranging from 0 ◦ to 360 ◦ , we converted it into the −180◦to 180◦format. It is also relevant to mention that the yaw returned from the sensor increases the reported value when turning clockwise, but we inverted the yaw so that it Data 2023,8, 157 16 of 20 mean error of 0.62 m [ 45 ], demonstrating that when combined with other techniques, dead reckoning enables accurate tracking of indoor vehicles. Table 4. Dead reckoning results from both IMUs (in metres). T1 T2 T3 T4 T5 T6 Overall IMU1 Mean 6.19 20.22 8.82 12.02 3.73 2.26 8.25 P75th 12.63 28.91 9.19 13.34 9.54 2.59 9.57 P99th 15.80 42.12 20.44 34.07 9.57 5.90 42.12 Max 15.80 42.12 20.44 34.16 9.57 5.92 42.12 IMU2 Mean 4.08 11.23 15.93 7.57 3.20 16.72 11.66 P75th 6.63 17.48 21.63 9.07 8.17 20.55 17.51 P99th 12.07 19.87 28.95 21.11 8.19 35.55 35.55 Max 12.07 19.87 28.95 21.17 8.20 35.55 35.56 0 10 20 30 40 50 Error (m) 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 Probability T1 T2 T3 T4 T5 T6 0 10 20 30 40 50 Error (m) 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 Probability T1 T2 T3 T4 T5 T6 (a) (b) Figure 13. CDF of dead-reckoning trajectories obtained with (a) IMU1 and (b) IMU2. 5.2. Wi-Fi Fingerprinting We computed the Wi-Fi fingerprinting positioning error for each track that was performed using single Wi-Fi interfaces and the merged data to compare how averaging Wi-Fi samples from multiple interfaces affects positioning results. We used k= 3 with the Manhattan (city-block) distance and evaluated the positioning error by determining the Euclidean distance between the ground truth and the estimated position. Since ground-truth positions are not synchronised with Wi-Fi samples, the positioning error was obtained only for the Wi-Fi samples that were closer in time for each of the ground-truth positions. The Wi-Fi fingerprinting results of trajectory T3 , obtained for each Wi-Fi interface and the interface with the averaged samples, are presented in Table 5. We opted for T3 to show these results because it is the longest trajectory, containing more Wi-Fi samples than the others. The merged Wi-Fi data Wm has the best 99th percentile and maximum errors in comparison to results with single interfaces. Although the best mean and 75th percentile errors are achieved with Wi-Fi interface W3 , averaging multiple Wi-Fi interfaces provides the best overall result, leading to improvements in the last percentile. Improvements are Data 2023,8, 157 17 of 20 significant in the maximum error reporting 8.04 m, with improvements ranging from 54% to 75% in comparison to single Wi-Fi interfaces. Table 6shows the positioning results of Wi-Fi fingerprinting for all trajectories, using the merged Wi-Fi data provided in Wm files. The last column aggregates position estimates from all trajectories with an overall mean error of 2.19 m, which outperforms other research works using Wi-Fi fingerprinting [9]. Table 5. Wi-Fi fingerprinting results of trajectory T3 using different Wi-Fi interfaces (in metres). W1W2W3W4Wm Mean 3.88 3.22 2.71 5.12 3.30 P75th 6.28 4.96 3.56 8.02 4.96 P99th 12.52 8.18 12.67 17.80 8.02 Max 15.05 14.04 14.96 17.80 8.04 Table 6. Wi-Fi fingerprinting results using averaged Wi-Fi samples from all interfaces (in metres). T1 T2 T3 T4 T5 T6 Overall Mean 2.00 1.84 3.30 1.24 2.68 1.88 2.19 P75th 3.36 3.16 4.96 2.14 3.93 2.68 3.60 P99th 7.88 6.62 8.02 5.22 7.82 12.79 8.01 Max 7.97 6.66 8.04 5.29 7.84 12.80 12.80 Figure 14 illustrates the CDF of these results, presenting the error distribution for each trajectory. Notably, trajectories T4 and T6 exhibit comparable performance up to the 75th percentile, achieving positioning errors of 2.14 m and 2.86 m, respectively. However, it is worth mentioning that despite having the second-best 75th percentile error, T6 demonstrates the highest positioning error, measuring 12.80 m. Although the maximum error of T6 is higher than the one observed in the other trajectories, it is a rare occurrence. This is evident from the fact that its 95th percentile positioning error is 7.89 m, which is similar to the maximum error seen in the other trajectories. Consequently, 95% of the evaluated position estimates lie below 7.89 m, thus being a more reliable indication of typical performance. 0 2 4 6 8 10 12 Error (m) 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 Probability T1 T2 T3 T4 T5 T6 Figure 14. CDF of Wi-Fi fingerprinting using averaged Wi-Fi samples. 6. Conclusions This paper presented a new dataset for the indoor tracking of vehicles in industrial environments. The dataset was collected in a factory-like environment with a mobile unit, which was purposely designed to emulate an industrial vehicle equipped with Wi-Fi and motion sensors. In addition to the documentation of the dataset, we detailed the process to Data 2023,8, 157 18 of 20 collect the data, describing the software used to collect data from each sensor type and how the GT data were obtained. Despite the existence of other datasets for indoor positioning, the dataset proposed in this paper contributes to the pool of available datasets with distinct characteristics: data were collected in an industrial environment with heavy machinery that can influence indoor radio propagation and magnetometer readings; Wi-Fi data (radio map and test samples) from multiple Wi-Fi interfaces; orientation data obtained from two low-cost IMUs; displacement data obtained from an absolute encoder attached to the wheel, allowing accurate measurement of the travelled distance; and GT collected by a video camera to annotate timestamps when the mobile unit passed by reference tags. Due to having data from two low-cost IMUs, this dataset can be explored to devise new sensor fusion approaches that combine data from both sensors to improve orientation estimates while keeping the overall cost of the system more economical. These IMUs allow greatly reducing the cost of the positioning system especially when compared to industrial-grade sensors, which are costly. Industry 4.0 and the IoT are the primary research areas that can benefit from this dataset. Potential applications include the development of vehicle tracking and monitoring systems, autonomous robot positioning, as well as indoor transportation systems for materials. Additionally, the indoor positioning and indoor navigation community can explore this dataset to develop, test, and evaluate their systems as well as contribute new methods for indoor tracking in industrial environments. Finally, adding a new public dataset contributes to the fair comparison and benchmarking between different solutions. Author Contributions: I.S., C.P. and A.M. designed the data collection solution and conducted the data collection campaign; I.S. performed the radio map site survey, developed the software for collecting the data, conceived the data formats, and wrote the scripts. C.P. designed and developed the solution to obtain the ground truth. I.S., C.P. and J.T.-S. wrote the original draft of the paper; I.S., C.P., J.T.-S. and A.M. participated in the review and editing of the paper. All authors have read and agreed to the published version of the manuscript. Funding: This work has been supported by FCT—Fundação para a Ciência e Tecnologia within the R&D Units Project Scope UIDB/00319/2020. Institutional Review Board Statement: Not applicable. Informed Consent Statement: Not applicable. Data Availability Statement: The dataset is available on https://doi.org/10.5281/zenodo.7826540, accessed on 19 October 2023. Conflicts of Interest: The authors declare no conflict of interest. Abbreviations The following abbreviations are used in this manuscript: AP Access Point BLE Bluetooth Low Energy CSV Comma-Separated Values GT Ground Truth GPS Global Positioning System IoT Internet of Things IPIN International Conference on Indoor Positioning and Indoor Navigation IPS Indoor Positioning System IMU Inertial Measurement Unit k-NN k-Nearest Neighbour RPi Raspberry Pi RSS Received Signal Strength RSSI Received Signal Strength Indicator UWB Ultrawide-Band Data 2023,8, 157 19 of 20 References 1. Aheleroff, S.; Xu, X.; Lu, Y.; Aristizabal, M.; Pablo Velásquez, J.; Joa, B.; Valencia, Y. IoT-enabled smart appliances under industry 4.0: A case study. Adv. Eng. Inform. 2020,43, 101043. [CrossRef] 2. Ssekidde, P.; Steven Eyobu, O.; Han, D.S.; Oyana, T.J. Augmented CWT Features for Deep Learning-Based Indoor Localization Using WiFi RSSI Data. Appl. Sci. 2021,11, 1806. [CrossRef] 3. Sittón-Candanedo, I.; Alonso, R.S.; Rodríguez-González, S.; García Coria, J.A.; De La Prieta, F. 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